LLM temperature definition
LLM temperature is a parameter that controls the level of randomness in a large language model’s responses. A lower temperature makes the model’s output more focused and predictable, while a higher temperature introduces more creativity and variability in its responses.
See also: training data, machine learning, end-to-end (E2E) learning, zero-shot learning (ZSL), data augmentation
How does LLM temperature work?
- Low temperature (≈0-0.3): The model sticks to its top choices — good for facts, code, instructions.
- Medium (≈0.4-0.8): Useful for rewrites, brainstorming, or drafts that still need to stay on track.
- High (≈0.9-1.2+): Creative and diverse, but more error‑prone. Try for poetry, ideas, or fiction but expect occasional drift.
Benefits of adjusting LLM temperature
- Lower temperatures give you more predictable, focused responses, while higher temperatures allow for more creative and varied outputs.
- You can adjust the temperature to suit the task at hand, whether you want factual precision or more imaginative, open-ended replies.
- By tweaking temperature, you can match the tone or style of the conversation, whether it’s formal, casual, or playful.
- High temperature can be useful for brainstorming, while low temperature is ideal for technical or structured tasks where accuracy is crucial.
- When a higher temperature is used, the responses feel more dynamic, which can keep conversations or content more engaging.
